DBMS > Apache Druid vs. mSQL vs. RocksDB vs. Spark SQL

System Properties Comparison Apache Druid vs. mSQL vs. RocksDB vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

mSQL (Mini SQL) is a simple and lightweight RDBMS

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Key-value store

Relational DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

druid.apache.org

hughestech.com.au/products/msql

rocksdb.org

spark.apache.org/sql

Technical documentation

druid.apache.org/docs/latest/design

github.com/facebook/rocksdb/wiki

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Apache Software Foundation and contributors

Hughes Technologies

Facebook, Inc.

Apache Software Foundation

Initial release

2012

1994

2013

2014

Current release

29.0.1, April 2024

4.4, October 2021

9.2.1, May 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache license v2

commercial

free licenses can be provided

Open Source

BSD

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Scala

Server operating systems

Linux
OS X
Unix

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux

Linux
OS X
Windows

Data scheme

yes

schema-less columns are supported

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

SQL

Support of SQL

SQL for querying

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

SQL-like DML and DDL statements

APIs and other access methods

JDBC
RESTful HTTP/JSON API

JDBC
ODBC

C++ API
Java API

JDBC
ODBC

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C
C++
Delphi
Java
Perl
PHP
Tcl

C
C++
Go
Java
Perl
Python
Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

horizontal partitioning

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

none

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Foreign keys

Referential integrity

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

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